Artificial intelligence adoption was associated with better financial risk-management performance in a cross-sectional study of 142 banking and financial institutions across Morocco, Egypt, Tunisia, Algeria and Libya. The analysis also found that data quality, risk-governance maturity and regulatory compliance were positively associated with the composite performance measure, while institution size was not. Because the study used one principal observation per institution, its results indicate statistical associations rather than establishing a strict causal direction between AI adoption and performance.

The research, published in Discover Artificial Intelligence, combined audited financial statements and central-bank supervisory information covering 2015–2024 with survey responses collected from chief risk officers and chief information officers in 2024. The initial pool contained 198 institutions. Researchers excluded 23 with incomplete or disrupted financial data, 12 undergoing major mergers, acquisitions or restructurings that would disrupt time-series continuity, and 21 with survey completeness below 80 per cent.

The final sample included commercial banks, specialised lenders, fintechs and insurers across the five countries. The stated observation period captured the gradual implementation of Basel III, the expansion of machine-learning credit scoring and fraud detection, and the COVID-19 disruption of 2020–2021. Despite financial records being available across the decade, the estimation was cross-sectional, using one recent complete observation per institution supplemented by the 2024 survey.

Its dependent variable, PERF_AI, combined two measured dimensions of risk performance: a 60 per cent-weighted reduction in the non-performing-loan ratio attributed to AI-enhanced credit scoring and a 40 per cent-weighted improvement in the area under the credit-scoring discrimination curve. Weights were derived through confirmatory factor analysis on a pilot subset of 40 institutions. Data quality combined completeness, reliability and freshness, with respective weights of 40, 35 and 25 per cent.

Governance maturity combined audit processes and oversight committees, each weighted 35 per cent, with reporting practices weighted 30 per cent. Regulatory compliance came from supervisory assessments and was cross-validated against institutional self-evaluations. Researchers estimated a multivariate ordinary least-squares model containing AI adoption, data quality, governance maturity, institution size and regulatory compliance.

The AI-adoption variable was the logarithm of AI investment as a proportion of total assets. Diagnostic tests did not indicate significant departures from linearity, normality, constant error variance or residual independence; variance-inflation factors ranged from 1.143 to 2.217, well below a threshold of 5 used to flag problematic multicollinearity. Cook’s distance reached a maximum of 0.087, below the stated influence threshold of 1.

The model explained 53.1 per cent of the variation in the composite performance score, with an adjusted R-squared of 0.516 and an F-statistic of 35.24. These diagnostics support the stability of the reported associations, but they do not resolve possible reverse causality or unmeasured confounding. AI adoption had the largest estimated association with risk-management performance.

Its coefficient was 8.312, with a 95 per cent confidence interval from 4.078 to 12.546 and a p-value below 0.001. The corresponding bivariate correlation with the performance measure was 0.68, also the strongest pairwise relationship reported. Because the predictor was log-transformed, the coefficient should not be interpreted as a percentage return on AI spending or as evidence that every additional unit of expenditure produces an 8.312-point performance gain.

Instead, with data quality, governance, regulatory compliance and size held constant, greater AI investment intensity was associated with a higher composite score. The wide confidence interval indicates substantial variation among institutions in the observed relationship. Data quality was also positively associated with performance.

A one-unit increase on the data-quality index corresponded to an estimated 0.318-unit increase in the composite score, with a 95 per cent confidence interval from 0.174 to 0.462. The index captured whether data were complete, reliable and fresh, qualities directly relevant to models that learn from historical information and assess transactions in near real time. The sample nevertheless showed wide differences: the mean data-quality score was 0.612, with a standard deviation of 0.148 and a range from 0.210 to 0.920.

Risk-governance maturity had a positive coefficient of 0.241, with a 95 per cent confidence interval of 0.107 to 0.375 and a p-value of 0.001. The measure covered audit processes, oversight committees and reporting practices. Survey information associated higher-scoring institutions with technical model validation, cross-functional review of model outputs, approval of major recalibrations and independent governance audits.

The study also identifies governance problems that such structures are intended to address, including model drift, overfitting to historical patterns, discriminatory outputs and optimisation against imperfect performance proxies. However, the cross-sectional design does not directly establish that governance preserves early AI gains over time, nor does it establish that oversight alone causes better performance; the result is an adjusted statistical association among institutions. Regulatory compliance had a positive coefficient of 0.187, with a 95 per cent confidence interval of 0.071 to 0.303 and a p-value of 0.002.

The mean compliance score was 0.673 and displayed the clearest country pattern: approximately 0.82 for Moroccan institutions, 0.71 for Egyptian institutions, 0.63 for Tunisian institutions, 0.52 for Algerian institutions and 0.39 for Libyan institutions. The measure was sourced from central-bank supervisory assessments and cross-validated against self-reports, with a reported correlation of 0.812. In a separate model combining Tunisia, Algeria and Libya, the regulatory coefficient was no longer significant at p = 0.124, a result the authors linked to the smaller subsample and limited regulatory variation.

Institution size, represented by the logarithm of total assets, had no statistically significant association with performance after the other variables were included. Its coefficient was 0.021, with a 95 per cent confidence interval from minus 0.014 to 0.056 and a p-value of 0.245. The size coefficient also remained non-significant in country and institution-type subsamples.

The null size result suggests that large balance sheets did not independently explain performance once AI adoption, data quality, governance and regulation were considered. The paper’s proposed mechanisms for this result include legacy-system complexity and slow organisational decision-making at large incumbents, but the cross-sectional analysis cannot directly demonstrate those pathways. The paper groups data quality, governance maturity and regulatory compliance into an AI Performance Enabling Ecosystem, describing them as mutually reinforcing institutional conditions associated with AI-driven risk performance.

The framework shifts attention from algorithm acquisition alone to the environment in which models operate. Reliable inputs matter because poor historical or current data constrain what a model can learn and how well its outputs can be applied. Governance provides structures for validation, recalibration, fairness monitoring and accountability, while supervisory frameworks can set expectations for documented, auditable risk systems.

The results are consistent with these mechanisms, but they do not show that AI “works best” only under one specific combination or establish causal amplification through a formal interaction model. In an augmented specification, the AI-adoption-by-data-quality interaction was positive but only marginally significant, at a coefficient of 2.14 and p = 0.063, leaving the relationship for testing in a larger sample. Several limitations constrain interpretation.

The cross-sectional structure prevents the use of within-institution fixed effects, lagged regressors or other designs needed for stronger causal identification, and the authors explicitly describe the estimates as robust statistical associations consistent with a causal interpretation rather than proof of causal direction. AI investment could follow management quality or other institutional advantages, and survey self-reporting may introduce social-desirability bias. The Libya subgroup contained only three institutions, preventing meaningful country-specific inference, while weighting choices in composite indicators introduce unavoidable measurement decisions.

The observational data also cannot determine whether particular AI applications caused reductions in non-performing loans, because that component depends on a benchmark attributed to AI-enhanced scoring. Despite these limits, the study offers a consistent regional picture in which AI adoption, strong institutional conditions and better measured risk performance occur together, while scale alone does not explain the cross-institution variation.

Subject of Research: Cross-sectional associations between AI adoption, institutional conditions and financial risk-management performance at 142 North African banking and financial institutions.

Article Title: Artificial intelligence in financial risk management empirical evidence from Morocco and North Africa

Article References: Attahiri, A., & Makhroute, M. (2026). Artificial intelligence in financial risk management empirical evidence from Morocco and North Africa. Discover Artificial Intelligence, 6(1), Article 1197. https://doi.org/10.1007/s44163-026-02233-9

Image Credits: AI Generated

DOI: 10.1007/s44163-026-02233-9

Keywords: artificial intelligence, financial risk management, North African banking, cross-sectional study, data quality, risk governance, regulatory compliance, credit scoring, ordinary least squares, emerging markets, Artificial, intelligence